Method and system for automatic quality control used in computerized analysis of CT angiography

ABSTRACT

A method and a system for identifying a data quality issue associated with medical diagnostic data is provided. The system includes, but is not limited to, an imaging apparatus configured to generate imaging data and a processor operably coupled to the imaging apparatus to receive the imaging data. The processor is configured to analyze the received imaging data; to identify a data quality issue associated with the received imaging data based on the analysis of the received imaging data; to generate a message including the identified data quality issue; and to send the generated message to a user.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority to U.S. Provisional Patent Application No. 60/862,912, filed on Oct. 25, 2006, and titled “Method And System For Automatic Analysis Of Blood Vessel Structures And Pathologies,” the contents of which are incorporated herein by reference in their entirety. Further, the present application is related to U.S. patent application Ser. No. 11/562,771 entitled “Method And System For Automatic Analysis Of Blood Vessel Structures And Pathologies In Support Of A Triple Rule-Out Procedure”, U.S. patent application Ser. No. 11/562,875, entitled “Method And System For Automatic Analysis Of Blood Vessel Structures And Pathologies”, U.S. patent application Ser. No. 11/562,897 entitled “Method And System For The Presentation Of Blood Vessel Structures And Identified Pathologies,” and U.S. patent application Ser. No. 11/562,906 entitled “Method And System For Automatic Analysis Of Blood Vessel Structures To Identify Pathologies,” the contents of which are incorporated herein by reference in their entirety.

FIELD

The field of the disclosure relates generally to computer systems. More specifically, the disclosure relates to automatic quality control used in computerized analysis of coronary CT angiography analysis.

BACKGROUND

Automatic computerized analysis of medical imaging data is becoming an increasingly important and acceptable method of working with vast amounts of data produced by modern medical diagnostic equipment. The reliability of the results produced by the automatic computerized analysis of the medical data is very important. The reliability and the robustness of automatic computerized analysis systems makes it either efficient or useless in a real medical world scenario.

No computer system can produce 100% correct results 100% of the time, which means that any automatic analysis system will fail eventually to analyze the data correctly. Such failures may be tolerable if the system automatically recognizes them and reports an inability to cope with the input to the operator. A failure report is more useful if the system provides additional details concerning the reason for the failure. For example, if the input data was of insufficient quality for automatic analysis, the input data is probably also of insufficient quality for a human to read as well. As a result, the medical test should be repeated.

In general, existing medical imaging data analysis tools do not provide a fully automatic solution and instead rely on the user's judgment concerning the quality of the result. As a result, these types of analysis tools assume a significant amount of user interaction, and thus, reserve the image understanding to the human operator. Another group of existing systems, which fall into the category of computer aided detection/diagnosis (CAD) tools, are usually more automatic and do include computerized image understanding, reasoning, and decision making. However, existing CAD systems lack a self-diagnosis capability, which could reduce the error rate by reporting cases where a reliable diagnosis is not possible and/or where the system is not confident in the validity of the result. Thus, a method and a system for an automatic self-diagnosis of medical CAD systems that allows automatic detection of cases in which automatic processing fails or produces unreliable results is needed. A method and a system for automatically quantifying an estimation of an image quality is also needed.

SUMMARY

A method and a system for automatic quality control (QC) for computer based analysis of medical imaging data is provided. In an exemplary embodiment, the medical imaging data is coronary computed tomography (CT) angiography (CTA) data. The method and system provide self-diagnosis of an automatic analysis process and provide assessment of a CT angiography study quality. The QC method and system automatically distinguish between cases for which a reliable result can be produced by the computerized analysis and those for which the automatic processing is impossible and/or a confidence in the result is low. In addition, the QC method and system provide an estimation of the image quality, which can be used independently by medical imaging equipment operators to measure the readability of the study and to calibrate the medical imaging equipment.

In an exemplary embodiment, a system for identifying a data quality issue associated with medical diagnostic data is provided. The system includes, but is not limited to, an imaging apparatus configured to generate imaging data and a processor operably coupled to the imaging apparatus to receive the imaging data. The processor is configured to analyze the received imaging data; to identify a data quality issue associated with the received imaging data based on the analysis of the received imaging data; to generate a message including the identified data quality issue; and to send the generated message to a user.

In another exemplary embodiment, a method of identifying a data quality issue associated with medical diagnostic data is provided. Imaging data is received at a computing device. The received imaging data is analyzed at the computing device. A data quality issue associated with the received imaging data is identified based on the analysis of the received imaging data at the computing device. A message including the identified data quality issue is generated at the computing device. The generated message is sent to a user.

In yet another exemplary embodiment, computer-readable instructions are provided that, upon execution by a processor, cause the processor to implement the operations of the method of identifying a data quality issue associated with medical diagnostic data

Other principal features and advantages of the invention will become apparent to those skilled in the art upon review of the following drawings, the detailed description, and the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments will hereafter be described with reference to the accompanying drawings, wherein like numerals denote like elements.

FIG. 1 depicts a block diagram of an automated CT image processing system in accordance with an exemplary embodiment.

FIGS. 2 a and 2 b depict a flow diagram illustrating exemplary operations performed by the automated CT image processing system of FIG. 1 in accordance with an exemplary embodiment.

FIG. 3 depicts a flow diagram illustrating exemplary operations performed in detecting a heart region in accordance with an exemplary embodiment.

FIG. 4 depicts a flow diagram illustrating exemplary operations performed in detecting a lung region in accordance with an exemplary embodiment.

FIGS. 5 a and 5 b depict a flow diagram illustrating exemplary operations performed in detecting and identifying the aorta in accordance with an exemplary embodiment.

FIGS. 6 a and 6 b depict a flow diagram illustrating exemplary operations performed in identifying coronary artery vessel exit points from the aorta in accordance with an exemplary embodiment.

FIGS. 7 a and 7 b depict a flow diagram illustrating exemplary operations performed in identifying coronary artery vessel tree in accordance with an exemplary embodiment.

FIG. 8 depicts a flow diagram illustrating exemplary operations performed in identifying calcium seed in accordance with an exemplary embodiment.

FIG. 9 depicts a flow diagram illustrating exemplary operations performed in identifying in identifying soft plaque in accordance with an exemplary embodiment.

FIG. 10 depicts a graph illustrating X-junction removal from a coronary artery vessel tree in accordance with an exemplary embodiment.

FIG. 11 depicts a first user interface of a visualization application presenting summary pathology results in accordance with an exemplary embodiment.

FIG. 12 depicts a second user interface of the visualization application presenting multiple views of a pathology in accordance with a first exemplary embodiment.

FIG. 13 depicts a third user interface of the visualization application presenting a blood vessel effective lumen area as a function of distance from the aorta in accordance with an exemplary embodiment.

FIG. 14 depicts a fourth user interface of the visualization application presenting multiple views of a pathology in accordance with a second exemplary embodiment.

FIG. 15 depicts a fifth user interface of the visualization application presenting multiple views of a pathology in accordance with a third exemplary embodiment.

FIG. 16 depicts a flow diagram illustrating exemplary operations performed in assessing a quality of medical imaging data in accordance with an exemplary embodiment.

DETAILED DESCRIPTION

With reference to FIG. 1, a block diagram of an image processing system 100 is shown in accordance with an exemplary embodiment. Image processing system 100 may include a computed tomography (CT) apparatus 101 and a computing device 102. Computing device 102 may include a display 104, an input interface 106, a memory 108, a processor 110, a pathology identification application 112, and a visualization application 114. In the embodiment illustrated in FIG. 1, CT apparatus 101 generates image data. In general, however, the present techniques are well-suited for use with a wide variety of medical diagnostic system modalities, including magnetic resonance imaging systems, ultrasound systems, positron emission tomography systems, nuclear medicine systems, etc. Moreover, the various modality systems may be of a different type, manufacture, and model. Thus, different and additional components may be incorporated into computing device 102. Components of image processing system 100 may be positioned in a single location, a single facility, and/or may be remote from one another. As a result, computing device 102 may also include a communication interface, which provides an interface for receiving and transmitting data between devices using various protocols, transmission technologies, and media as known to those skilled in the art. The communication interface may support communication using various transmission media that may be wired or wireless.

Display 104 presents information to a user of computing device 102 as known to those skilled in the art. For example, display 104 may be a thin film transistor display, a light emitting diode display, a liquid crystal display, or any of a variety of different displays.

Input interface 106 provides an interface for receiving information from the user for entry into computing device 102 as known to those skilled in the art. Input interface 106 may use various input technologies including, but not limited to, a keyboard, a pen and touch screen, a mouse, a track ball, a touch screen, a keypad, one or more buttons, etc. to allow the user to enter information into computing device 102 or to make selections presented in a user interface displayed on display 104. Input interface 106 may provide both an input and an output interface. For example, a touch screen both allows user input and presents output to the user.

Memory 108 is an electronic holding place or storage for information so that the information can be accessed by processor 110 as known to those skilled in the art. Computing device 102 may have one or more memories that use the same or a different memory technology. Memory technologies include, but are not limited to, any type of RAM, any type of ROM, any type of flash memory, etc. Computing device 102 also may have one or more drives that support the loading of a memory media such as a compact disk or digital video disk.

Processor 110 executes instructions as known to those skilled in the art. The instructions may be carried out by a special purpose computer, logic circuits, or hardware circuits. Thus, processor 110 may be implemented in hardware, firmware, software, or any combination of these methods. The term “execution” is the process of running an application or the carrying out of the operation called for by an instruction. The instructions may be written using one or more programming language, scripting language, assembly language, etc. Processor 110 executes an instruction, meaning that it performs the operations called for by that instruction. Processor 110 operably couples with display 104, with input interface 106, with memory 108, and with the communication interface to receive, to send, and to process information. Processor 110 may retrieve a set of instructions from a permanent memory device and copy the instructions in an executable form to a temporary memory device that is generally some form of RAM. Computing device 102 may include a plurality of processors that use the same or a different processing technology.

Pathology identification application 112 performs operations associated with the analysis imaging data to identify and evaluate blood vessel structures and to identify and evaluate pathologies associated with the identified blood vessel structures. Some or all of the operations and interfaces subsequently described may be embodied in pathology identification application 112. The operations may be implemented using hardware, firmware, software, or any combination of these methods. With reference to the exemplary embodiment of FIG. 1, pathology identification application 112 is implemented in software stored in memory 108 and accessible by processor 110 for execution of the instructions that embody the operations of pathology identification application 112. Pathology identification application 112 may be written using one or more programming languages, assembly languages, scripting languages, etc. Pathology identification application 112 may integrate with or otherwise interact with visualization application 114.

Visualization application 114 performs operations associated with presentation of the blood vessel analysis and identification results to a user. Some or all of the operations and interfaces subsequently described may be embodied in visualization application 114. The operations may be implemented using hardware, firmware, software, or any combination of these methods. With reference to the exemplary embodiment of FIG. 1, visualization application 114 is implemented in software stored in memory 108 and accessible by processor 110 for execution of the instructions that embody the operations of visualization application 114. Visualization application 114 may be written using one or more programming languages, assembly languages, scripting languages, etc.

CT apparatus 101 and computing device 102 may be integrated into a single system such as a CT imaging machine. CT apparatus 101 and computing device 102 may be connected directly. For example, CT apparatus 101 may connect to computing device 102 using a cable for transmitting information between CT apparatus 101 and computing device 102. CT apparatus 101 may connect to computing device 102 using a network. In an exemplary embodiment, computing device 102 is connected to a hospital computer network and a picture archive, and communication system (PACS) and receives a CT study acquired on CT apparatus 101. Using PACS, CT images are stored electronically and accessed using computing device 102. CT apparatus 101 and computing device 102 may not be connected. Instead, the CT study acquired on CT apparatus 101 may be manually provided to computing device 102. For example, the CT study may be stored on electronic media such as a compact disc or a digital video disc. After receiving the CT study, computing device 102 may initiate automatic processing of the set of images that comprise the CT study. In an exemplary embodiment, CT apparatus 101 is a 64-slice multi-detector advanced CT scanner having a reconstructed slice width and inter-slice distance less than or equal to approximately 0.5 millimeters (mm), which produces standard digital imaging and communications in medicine (DICOM) images. Computing device 102 may be a computer of any form factor.

Image processing system 100 may provide an initial classification and decision support system, which allows fast and accurate ruling out of the three major diseases associated with chest pain. Image processing system 100 can be provided as a primary CT study inspection tool assisting an emergency room physician. Additionally, image processing system 100 can be used either to completely rule out some or all of the three diseases (in case of negative results) or as a trigger to call a radiologist and/or a cardiologist to further analyze the case. Additionally, image processing system 100 may automatically identify and segment blood vessel trees and automatically analyze each blood vessel to detect and map all relevant pathologies, including calcified and soft plaque lesions and their degree of stenosis.

With reference to FIGS. 2 a and 2 b, exemplary operations associated with pathology identification application 112 and visualization application 114 of FIG. 1 are described. Additional, fewer, or different operations may be performed, depending on the embodiment. The order of presentation of the operations is not intended to be limiting. In an operation 200, pathology identification application 112 receives CT image data. The CT image data may be received from CT apparatus 101 directly or using a network. The CT image data also may be received using a memory medium. In an operation 202, a heart region is identified from the received CT image data. Data associated with the identified heart region is stored at computing device 102. In an exemplary embodiment, the data associated with the identified heart region includes a heart bounding box.

With reference to FIG. 3, exemplary operations associated with identifying the heart region data are described in accordance with an exemplary embodiment. Additional, fewer, or different operations may be performed, depending on the embodiment. The order of presentation of the operations is not intended to be limiting. For larger studies that may include head and neck or abdominal regions, a determination of the heart region provides a correct anatomical starting point for further segmentation. For smaller studies, a determination of the heart region reduces the size of the processed region and removes the non-relevant areas to reduce the false alarm risk. In an exemplary embodiment, the top and bottom boundaries of the heart region are cut a predetermined distance above and below the heart center.

In an operation 300, a first projection into an X-Y plane is defined. A positive X-axis is defined as extending out from the left side of the body. A positive Y-axis is defined as extending out from the back side of the body. A positive Z-axis is defined as extending out from the head of the body. The first projection is defined by summing the DICOM series along the Z-axis. In an operation 302, a threshold is applied to the first projection. For example, a threshold greater than approximately zero may be applied to eliminate the negative values of air which dominate the region outside the heart region and to retain the positive Hounsfeld unit (HU) values which include the fat, blood, and bones within the heart region. In an operation 304, a first largest connect component (CC) is identified in the thresholded first projection. In an operation 306, a first center of mass of the first largest CC is determined and denoted as X_(c), Y_(c1).

In an operation 308, a second projection into a Y-Z plane is defined. The second projection is defined by summing the DICOM series along the X-axis. In an operation 310, a threshold is applied to the second projection data. For example, a threshold greater than approximately zero may be applied to eliminate the negative values of air which dominate the region outside the heart region and to retain the positive HU values which include the fat, blood, and bones within the heart region. In an operation 312, a second largest CC is identified in the thresholded second projection data. In an operation 314, a second center of mass of the second largest CC is determined and denoted as Y_(c2), Z_(c). A heart region center is defined as X_(c), Y_(c1), Z_(c). In an operation 316, a heart region bounding box is defined from the heart region center and an average heart region width in each axis direction, W_(X), W_(Y), W_(Z).

In an operation 318, the defined heart region is compared to a quality control parameter associated with a valid heart region. For example, the size and the position of the defined heart region is compared with an expected size and position within the CT study. Additionally, a tissue type, a contrast, and arterials inside the defined heart region may be compared with the expected tissue type, contrast, and arterials inside a heart region. In an operation 320, a determination is made concerning whether or not the heart region data is valid. If the heart region data is valid, processing continues at an operation 322. If the heart region data is not valid, processing continues at an operation 324. In operation 322, the defined heart region bounding box is stored at computing device 102. The X-axis, Y-axis, Z-axis system centered at X_(c), Y_(c1), Z_(c) defines a body coordinate system. In operation 324, a message is output to the user. For example, a message may be printed from computing device 102, may be displayed at computing device 102, may played audibly from computing device 102, may be transmitted to the user (i.e., e-mail, text message, instant message, voice message, etc.), etc. The message may include an error indication that identifies a source of the invalidity or unreliability determination. For example, the message may indicate an invalidity of the heart region determination. In an operation 326, processing of the CT image data stops.

With reference again to FIG. 2, in an operation 204, a lung region is identified from the received CT image data. Data associated with the identified lung region is stored at computing device 102. In an exemplary embodiment, the data associated with the identified lung region may include a lung mask. With reference to FIG. 4, exemplary operations associated with identifying the lung region data are described in accordance with an exemplary embodiment. Additional, fewer, or different operations may be performed, depending on the embodiment. The order of presentation of the operations is not intended to be limiting.

In an operation 400, a lung threshold is applied to each slice of the DICOM series data. For example, a lung threshold of −400 HU may be applied. In an operation 402, a morphological filter is applied to the binary image. In an exemplary embodiment, the binary image is filtered using a morphological closing operation to define a lung region in the CT image data. Other processes for filling holes in the image may be used as known to those skilled in the art. In an operation 404, a lung region is defined. In an operation 406, the defined lung region is compared to a quality control parameter associated with a valid lung region. For example, the intensity levels and patterns inside the defined lung region are compared with expected intensity levels and patterns in an lung region. In an operation 408, a determination is made concerning whether or not the lung region data is valid. If the lung region data is valid, processing continues at an operation 410. If the lung region data is not valid, processing continues at operation 324 shown with reference to FIG. 3 wherein the message indicates an invalidity of the lung region determination. In operation 410, the defined lung region is stored at computing device 102.

With reference again to FIG. 2, in an operation 206, a thorax region is identified from the received CT image data. Data associated with the identified thorax region is stored at computing device 102. The thorax region may be defined as a convex hull of the lungs and the diaphragm. In an operation 208, the pulmonary arteries are identified from the received CT image data. Data associated with the identified pulmonary arteries are stored at computing device 102.

In an operation 210, the received CT image data is preprocessed. For example, preprocessing may include image enhancement, smoothing, noise reduction, acquisition artifacts detection, etc. Examples of image enhancement algorithms include Gaussian smoothing, median filtering, bilateral filtering, anisotropic diffusion, etc. In an operation 211, image quality test data is calculated to determine a quality of the image. In an operation 212, the calculated image quality test data is compared to quality control parameters associated with quality image data. In an operation 213, a determination is made concerning whether or not the image quality is satisfactory. An exemplary process for evaluating a quality of the image data is shown with reference to FIG. 16. If the image quality is satisfactory, processing continues at an operation 214. If the image quality is unsatisfactory, processing continues at operation 324 shown with reference to FIG. 3 wherein the message indicates an unsatisfactory image quality.

In an operation 214, the left and right main pulmonary artery trees are defined. In an operation 216, the lumen of the left and right main pulmonary artery trees is analyzed to identify any pulmonary embolism candidates. In an operation 218, any pulmonary embolism candidates are classified. In an operation 220, possible pulmonary embolism lesions are identified.

In an operation 222, the ascending and the visible part of the abdominal aorta are segmented. In an operation 224, the lumen of the abdominal aorta is segmented. In an operation 226, a 3-D geometry of the abdominal aorta is modeled. In an operation 228, the modeled aorta is compared to a hypothesized “normal” abdominal aorta to identify deviations from the hypothesized “normal” abdominal aorta. In an operation 230, suspicious locations are detected and analyzed to identify dissections and aneurysms.

In an operation 232, the aorta is identified. The aorta is detected in the first imaging slice of the heart region bounding box based, for example, on intensity and shape properties including circularity, compactness, and area. The remainder of the aorta is identified by moving from slice to slice and looking for a similar 2-D object in each slice. Data associated with the identified aorta is stored at computing device 102. In an exemplary embodiment, the data associated with the identified aorta includes an aorta shape and boundary in the identified heart region. It is assumed that the heart region bounding box detected on the previous step includes the aorta exit from the heart and that the cross section of the aorta in the upper slice of the heart region is approximately circular.

With reference to FIGS. 5 a and 5 b, exemplary operations associated with identifying the aorta are described in accordance with an exemplary embodiment. Additional, fewer, or different operations may be performed, depending on the embodiment. The order of presentation of the operations is not intended to be limiting. In an operation 500, a first slice is selected from the heart region data. In an operation 502, an aorta threshold is applied to the first slice of the DICOM series data. For example, a lung threshold of 200 HU may be used. In an operation 504, a morphological filter is applied to the binary image. In an exemplary embodiment, the binary image is filtered using a series of morphological filtering operators including an opening operator using a first parameter. In an operation 506, one or more CCs are identified.

In an operation 508, a compactness of each identified CC is determined. In an operation 510, identified CCs having a determined compactness that exceeds a compactness threshold are eliminated from further consideration. A compactness measure of a shape is a ratio of the area of the shape to the area of a circle (the most compact shape) having the same perimeter. The ratio may be expressed mathematically as M=4π(area)/2(perimeter). In an exemplary embodiment, the compactness threshold is 0.75. In an operation 512, a size of each identified connected component is determined. In an operation 514, identified CCs having a size that exceeds a maximum size threshold or that is below a minimum size threshold are eliminated from further consideration. In an exemplary embodiment, the maximum size threshold is 10,000 pixels. In an exemplary embodiment, the minimum size threshold is 1,000 pixels. In an operation 516, a determination is made concerning whether or not any identified CCs remain for consideration. If identified CCs remain for consideration, processing continues at an operation 518. If no identified CCs remain for consideration, processing continues at an operation 520. In operation 518, an initial aorta candidate is selected from the remaining CCs. For example, if a plurality of identified CCs remain for consideration, the largest candidate CC that is foremost in the body is selected as the initial aorta candidate.

In operation 520, a next slice is selected from the heart region data. In an operation 522, the aorta threshold is applied to the next slice of the DICOM series data. In an operation 524, the morphological filter is applied to the binary image. In an operation 526, one or more CCs are identified. In an operation 528, a compactness of each identified CC is determined. In an operation 530, the identified CCs having a determined compactness that exceeds the compactness threshold are eliminated from further consideration. In an operation 532, a size of each identified connected component is determined. In an operation 534, the identified CCs having a size that exceeds the maximum size threshold or that is below the minimum size threshold are eliminated from further consideration. In an operation 536, a determination is made concerning whether or not any identified CCs remain for consideration in the current slice. If identified CCs remain for consideration, processing continues at an operation 538. If no identified CCs remain for consideration, processing continues at operation 520.

In operation 538, the identified CCs from the current slice are compared with the aorta candidate object(s) created from the previous slice(s). In an operation 540, a determination is made concerning whether or not any identified CCs match CCs identified from the previous slices. In an operation 542, if a match is found between a CC and an aorta candidate object, the matched CC is assigned to the aorta candidate object. For example, if a center of a CC is closer than twenty pixels to the center of an aorta candidate object, the CC may be identified as matched with the aorta candidate object. In an operation 544, if a match is not found between a CC and an aorta candidate object, a new aorta candidate object is created based on the CC.

In an operation 546, a determination is made concerning whether or not all of the slices have been processed. If slices remain, processing continues at operation 520. If no slices remain, in an operation 548, aorta candidate objects are eliminated based on length. For example, aorta candidate objects that persist for less than 20 slices may be removed from further consideration. In an operation 550, an aorta object is selected from the remaining aorta candidate objects. For example, the aorta candidate object closest to the upper left corner of the image may be selected as the aorta object. In an operation 552, a bounding box is defined around the selected aorta object to identify a region in which the aorta is located in the CT image data. In an operation 554, test data associated with the selected aorta object are calculated. For example, geometrical parameters associated with the selected aorta object and its relative position inside the heart bounding box are measured and/or calculated as test data. In an operation 556, the calculated test data are compared to a quality control parameter associated with a valid aorta object. For example, the diameter of the aorta should not change too much from the aortic arch to the entry point to the heart. Additionally, aorta cross sections should be approximately circular with a diameter within a valid range such as from 20 mm to 50 mm. Some or all of these parameters may be used as quality control parameters associated with determining if a selected aorta object is valid. In an operation 558, a determination is made concerning whether or not the selected aorta object is valid. If the selected aorta object is valid, processing continues at an operation 560. If the selected aorta object is not valid, processing continues at operation 324 shown with reference to FIG. 3 wherein the message indicates an invalidity of the aorta object determination. In operation 560, the selected aorta object is stored at computing device 102.

With reference again to FIG. 2, in an operation 234, exit points of the coronary arteries from the aorta are identified by evaluating all structures connected to the aorta object which look like a vessel. The direction of the vessel near a link point with the aorta object should be roughly perpendicular to an aorta centerline. Additionally, the left and right coronary arteries are expected to exit from the aorta object in a certain direction relative to the body coordinate system. If there are several exit point candidates, the exit point candidate which leads to a larger blood vessel tree is selected. It is assumed that the selected aorta object includes the points where the left and right coronary trees connect with the aorta.

With reference to FIG. 6, exemplary operations associated with identifying the exit points of the coronary arteries from the aorta object are described in accordance with an exemplary embodiment. Additional, fewer, or different operations may be performed, depending on the embodiment. The order of presentation of the operations is not intended to be limiting. Imaging slices including the aorta bounding box and a mask of the aorta detected at a previous slice are processed to detect the aorta at the current slice. In an operation 600, a first slice is selected from the aorta object. In an operation 602, regions are segmented based on a segmentation threshold at the detected aorta edges. The segmentation threshold may be calculated from the median value of pixels of the smoothed image at the detected edges. A ring of pre-defined radius is defined around the aorta edges detected on the previous slice and the edges are found in the ring on the current slice. Small edges are removed from further consideration. The segmentation threshold may be selected adaptively. For example, if no edges are found in the ring using the calculated segmentation threshold, the calculated segmentation threshold is reduced by half, and the procedure is repeated. If no edges are found using the lowered segmentation threshold, the calculated segmentation threshold is used for subsequent slices.

In an operation 604, the segmented image is post-processed. For example, small segmented objects are removed, possible vessels are removed from the segmented aorta candidates, and the segmented aorta candidates are intersected with the aorta detected in the previous slice. In an operation 606, the aorta candidates are validated by ensuring that there is at least one candidate that intersected the aorta detected in the previous slice and by ensuring that the aorta does not grow too fast. For example, if the aorta size in both a previous and a current slice is larger than 1500 pixels, the size growth ratio may be limited to 1.4. In an operation 608, the aorta candidates are selected. For example, CCs with a small intersection with the previously detected aorta are removed from consideration, and the upper-left-most candidate is chosen if a plurality of aorta candidates exist in the current slice. In an operation 610, the compactness of the selected aorta candidate is checked to ensure that the candidate is not compact. If the aorta candidate is not compact, the aorta search window is limited for the next slice. If the aorta candidate is compact, the whole image is used to search for the aorta in the next slice. In an operation 612, a bounding box for the aorta is calculated. If the aorta candidate is not compact, the bounding box size may be fixed and only the position of the bounding box updated to compensate for aorta movement. If the aorta candidate is compact, the bounding box may be attached to the upper left side of the aorta.

In an operation 614, a vesselness score is calculated for each voxel of the aorta object. As known to those skilled in the art, the vesselness score can be determined using a vesselness function. A vesselness function is a widely used function based on the analysis of Hessian eigen values. A good description of an exemplary vesselness function can be found for example in Frangi, A. F., Niessen, W. J., Vincken, K. L. and Viergever, M. A., 1998, “Multiscale Vessel Enhancement Filtering”, MICCAI'98, LNCS 1496, pp. 130-137. In an operation 616, a vesselness threshold is applied to the calculated vesselness score to identify possible exit points based on the HU value of a ring around the aorta object. Pixels in a ring around the detected aorta are grouped into CCs, which are analyzed to choose the most probable candidates for coronary tree exit points. In an operation 618, possible exit points are filtered to remove false candidates. For example, the possible exit points may be filtered based on a size of the CC corresponding to the possible exit, a location of the CC relative to the aorta, an incident angle of the CC relative to the aorta, etc. In an operation 620, a determination is made concerning whether or not any exit points are left. If no exit points are left for this slice, processing continues at an operation 624. If one or more exit points are left for this slice, processing continues at an operation 622. In operation 622, the one or more exit points left for this slice are added to a possible exit points list. In an operation 624, a determination is made whether or not the last slice has been processed. If the last slice has not been processed, processing continues at an operation 626. In operation 626, the next slice is selected from the aorta object data and processing continues at operation 602.

If the last slice has been processed, processing continues at an operation 628. In operation 628, a CC is identified for each exit point included in the possible exit points list. In an operation 630, a volume is calculated for each exit point CC (EPCC). In an operation 632, any EPCC having a volume below a volume threshold is eliminated from further consideration as an exit point. For example, the volume threshold may be 1500 voxels. In an operation 634, a maximum width of each EPCC is calculated. In an operation 636, any EPCC having a maximum width below a width threshold is eliminated from further consideration as an exit point. For example, the width threshold may be 2 mm. In an operation 638, a distance to the aorta is calculated for each EPCC. In an operation 640, the EPCC having a minimum distance to the aorta is selected. In an operation 642, a determination is made concerning whether or not a plurality of EPCCs remain. If a plurality of EPCCs remain, processing continues at an operation 644. If a plurality of EPCCs do not remain, processing continues at an operation 646. In operation 644, the EPCC having a maximum width is selected from the plurality of EPCCs remaining. In operation 646, the exit point is identified from the selected EPCCs.

In an operation 648, the geometry of the selected EPCC including a change of the vessel radius and the position and orientation of the selected EPCC in the heart bounding box are analyzed. In an operation 650, the analysis data are compared to a quality control parameter associated with a valid EPCC object. For example, a valid EPCC object should extend for a certain minimal distance from the exit point. An exemplary minimum distance is two centimeters. Additionally, if the EPCC object is indeed a tree of vessels, its convex hull to volume ratio also should be relatively low. The width of the structures in the EPCC object also should be low compared to a maximal expected radius of the blood vessel such as three mm. The EPCC corresponding to the right coronary tree should approximately cover the statistically expected right coronary tree region. The same should be true for the left coronary tree. Some or all of these parameters may be used as quality control parameters associated with determining if a selected EPCC object is valid. In an operation 652, a determination is made concerning whether or not the selected EPCC object is valid. If the selected EPCC object is valid, processing continues at an operation 654. If the selected EPCC object is not valid, processing continues at operation 324 shown with reference to FIG. 3 wherein the message indicates an invalidity of the EPCC determination.

With reference again to FIG. 2, in an operation 236, a coronary artery vessel tree is defined. For a traversed section of the blood vessel tree, a set of end points is identified, and an attempt is made to continue tracking beyond the end point in the direction of the corresponding tree branch. If an additional tree segment is detected, it is connected to the traversed tree, and the processing continues recursively. The process is finished when no branch can be continued. The stopping condition may result in connecting a wrong structure to the coronary tree (e.g. a vein or some debris in a noisy CT study). As a result, the successfully tracked vessels are identified and those which remain to be detected are identified. For example, which blood vessels are to be segmented (e.g. the left coronary artery (LCA), the right coronary artery (RCA), the left main coronary artery (LM), the middle portion of the left anterior descending coronary artery (LAD), and the proximal portion of the left circumflex coronary artery (LCX) and others) may be defined as an input to the process. Additionally, a maximum vessel length to track (e.g. five centimeters from the aorta) and a minimum blood vessel diameter to continue tracking also may be defined as inputs to the process. The location of some blood vessels may be based on anatomical landmarks. For example, the RCA is located in the right atrio-ventricular plane. These anatomical landmarks, which may be collated through an anatomical priors processing operation, allow false structures in the “wrong” places to be discarded and support the location of lost branches in the “right” places. A graph representation of the segmented vessel tree can be built from the identified end points and bifurcation points. Graph nodes are the end points and the bifurcation points. The edges are segments of a vessel centerline between the nodes.

With reference to FIGS. 7 a and 7 b, exemplary operations associated with defining the coronary artery vessel tree are described in accordance with an exemplary embodiment. Additional, fewer, or different operations may be performed, depending on the embodiment. The order of presentation of the operations is not intended to be limiting. In an operation 700, the vesselness score data is received. In an operation 702, a first binary volume is defined for a first threshold. The first binary volume includes a ‘1’ for each voxel that exceeds the first threshold and a ‘0’ for each voxel that does not exceed the first threshold. In an operation 704, a second binary volume is defined for a second threshold. The second binary volume includes a ‘1’ for each voxel that exceeds the second threshold and a ‘0’ for each voxel that does not exceed the second threshold. The second threshold has a higher HU value than the first threshold. In an operation 706, one or more CCs in the first binary volume that intersect voxels from the second binary volume are selected as the one or more vessel CCs (VCCs). In an exemplary embodiment, intersection may be determined based on a spatial proximity between the CCs. In an exemplary embodiment, the first threshold and the second threshold are selected based on a statistical analysis of the input data such that the amount of voxels above the first threshold is approximately 0.15% of the total number of voxels in the volume and such that the amount of voxels above the second threshold is approximately 0.45% of the total number of voxels.

In an operation 708, the selected VCCs are labeled in the first binary volume. In an operation 710, a starting point or root is selected for a first VCC. In an operation 712, a width map is calculated for the first VCC. The width map includes the width of the VCC or the inverse distance from any point in the VCC to the boundary of the VCC. Thus, small values are near the centerline of the VCC and larger values are at the edges of the VCC. In an operation 714, the exit point of the selected VCC is identified in the binary volume. The RCA tree has a single exit point. Additionally, the LM tree has a single exit point. In an operation 715, the exit point of the selected VCC is compared to a quality control parameter associated with a valid exit point. For example, a relative position along the aorta of the exit point of the selected VCC and a size of the tubular structure components connected to the selected VCC are compared with expected values. Additionally, statistics on the positions of certain blood vessels relative to the heart related coordinate system may be collected over a large database of studies. Any given VCC may be compared to the derived statistically expected location of the blood vessels and the likelihood of the VCC being that vessel is computed. Some or all of these parameters may be used as quality control parameters associated with determining if a selected exit point is valid. In an operation 716, a determination is made concerning whether or not the exit point is valid. If the exit point is valid, processing continues at an operation 717. If the exit point is not valid, processing continues at operation 324 shown with reference to FIG. 3 wherein the message indicates an invalidity of the exit point determination.

In an operation 717, a distance map is calculated for the first VCC. The distance is calculated from any point in the VCC to the identified exit point. The distance map includes the calculated distance weighted by the width to ensure that the minimal path follows the vessel centerline. In an operation 718, candidate endpoints are identified. For example, during the calculation of the weighted distance map, one or more candidate endpoints may be saved. The candidate endpoints are voxels, which did not update any of their neighbors during the distance map calculation. In an operation 720, non-local maxima candidate endpoints are filtered. Thus, the candidate endpoints are scanned and only local maxima with respect to the distance from the root over a given window are kept. This process eliminates candidates that are not true blob vessel end points.

In an operation 722, an identifier for the candidate endpoint is created. In an operation 724, a new vertex is added to a symbolic graph of the vessel tree. In an operation 726, a first candidate endpoint is backtracked to the root to create graph edges. An auxiliary volume is used to mark voxels that have already been visited. The back-tracking may be a gradient descent iterative process (the gradient is in the distance field). Because the weighted distance map contains a single global minimum (the root), convergence is guaranteed. The method used to define the distance map ensures that the backtracking will be along the centerline or close to it. In an operation 728, all visited voxels in the auxiliary volume are marked with the current vertex identifier during the backtracking.

In an operation 730, a determination is made concerning whether or not a root is reached. If a root is reached, processing continues at an operation 732. If a root is not reached, processing continues at an operation 734. In operation 732, a new edge and vessel path are defined based on the backtracking. Processing continues at an operation 740. In an operation 734, a determination is made concerning whether or not an already visited voxel is reached. If an already visited voxel is reached, processing continues at an operation 736. If an already visited voxel is not reached, processing continues at an operation 726 to continue the backtracking to the endpoint. In an operation 736, a new edge and a new bifurcation vertex are defined. In an operation 738, the new bifurcation vertex is connected to the currently backtracked path, and the new edge and the new bifurcation vertex are added to the vessel tree.

In an operation 742, a determination is made concerning whether or not the endpoint is a leaf of the vessel tree or a vessel disconnected due to a low vesselness measure. In an exemplary embodiment, the determination is made based on the direction of the vessel at the endpoint and by searching for another VCC in a vacancy that contains a nearby endpoint. If the endpoint is a leaf, processing continues at operation 722 to create a new graph. The two graphs are joined together. If the endpoint is not a leaf, processing continues at an operation 744. In an operation 744, a determination is made concerning whether or not the last endpoint has been processed. If the last endpoint has not been processed, processing continues at operation 722.

If the last endpoint has been processed, processing continues at an operation 746. In operation 746, short branches are removed based on the rationale that they do not contribute to the analysis because important findings are usually located at the major blood vessels, which are thick and elongated. Therefore, in an exemplary embodiment, graph edges which lead to endpoints that are less than a length threshold are removed. An exemplary length threshold is five mm. In an operation 748, x-junctions are removed to eliminate veins. Veins are usually faint and spatially close to the arteries. X-junctions are defined as two very close bifurcation points. For example, close bifurcation points may be less than approximately 3 mm from each other. In an exemplary embodiment, a bifurcation may be two VCCs intersecting at angles between approximately 70 degrees and approximately 110 degrees. Additionally, a bifurcation may be two VCCs intersecting at angles approximately equal to 90 degrees. The sub-tree which remains is the one which has the closest direction to the edge arriving from the aorta.

For example, with reference to FIG. 10, a vessel tree 1000 includes a first vessel path 1002, a second vessel path 1004, and a third vessel path 1006. First vessel path 1002 and second vessel path 1004 form a first x-junction 1008. First vessel path 1002 has the closest direction to the edge arriving from the aorta and is selected to remain in the vessel tree. Second vessel path 1004 is removed. First vessel path 1002 and third vessel path 1004 form a second x-junction 1010. Again, first vessel path 1002 has the closest direction to the edge arriving from the aorta and is selected to remain in the vessel tree. Third vessel path 1006 is removed.

With reference again to FIGS. 7 a and 7 b, in an operation 750, single entry, single exit point vertices are removed. These vertices are created when one of the endpoints is recursively continued. The vertex is removed, and the two edges are joined to a single path. In an operation 752, a determination is made concerning whether or not all VCCs have been processed. If all VCCs have been processed, processing continues at an operation 754. If all VCCs have not been processed, processing continues at operation 710. In operation 754, blood vessel test data are calculated. For example, a volume and a length of the detected blood vessels is calculated. In an operation 756, the calculated blood vessel test data are compared to a quality control parameter associated with a valid blood vessel tree. For example, the presence of blood vessels in the expected anatomical locations such as the atrio-ventricular grooves and inter-ventricular plane is evaluated. The RCA is expected to go through the atrio-ventricular groove between the right ventricle and the right atrium. The groove may be automatically detected, and the RCA candidate vessel's location compared to the groove mask. If a significant part of the vessel is located outside the groove, or, alternatively, if a large part of the groove is not covered by the vessel, the vessel may be determined not to be the RCA. In an operation 758, a determination is made concerning whether or not the blood vessel tree is valid. If the blood vessel tree is valid, processing continues at an operation 760. In operation 760, processing continues. If the exit point is not valid, processing continues at operation 324 shown with reference to FIG. 3 wherein the message indicates an invalidity of the blood vessel tree determination.

With reference again to FIG. 2, in an operation 238, the defined coronary artery vessel tree is labeled. A list of graph edges (vessel segments) may be assigned to each blood vessel tracked by analyzing the relative section positions and locations relative to detected anatomical heart landmarks. In an operation 240, blood vessel test data are analyzed to determine a validity of the coronary artery tree labeling process. For example, the presence of the major coronary arteries such as LCA, RCA, LM, LAD, and LCX and their relative positions are analyzed. In an operation 242, the analyzed data is compared to a quality control parameter associated with valid blood vessel positions. For example, it is expected that the RCA and the LCX are approximately in one plane (RCA-LCX plane) and that the remaining blood vessels are on one side of the RCA-LCX plane. It further is expected that the LAD is approximately perpendicular to the RCA-LCX plane. In an operation 244, a determination is made concerning whether or not the blood vessel labeling process is valid. If the blood vessel labeling process is valid, processing continues at an operation 246. If the blood vessel labeling process is not valid, processing continues at operation 324 shown with reference to FIG. 3 wherein the message indicates an invalidity of the blood vessel labeling process. The blood vessel tree represented by a centerline for each blood vessel segment is stored at computing device 102.

In an operation 246, a radius of each blood vessel is determined. In an operation 248, a blood vessel centerline is determined. “Sausages” of blood vessels are obtained from the coronary artery vessel tree. Each “sausage” includes axial blood vessel cross-sections taken perpendicular to the blood vessel direction. Initially, a blood vessel center is presumed to be at the center of each section. Either “stretched” or “curved” blood vessels can be used. Any blood vessel radius and center line estimation method can be used. In an exemplary embodiment, low pass post-filtering between consecutive cross-sections is performed. Because a blood vessel may be surrounded by tissue having similar attenuation values, indirect indicators may be used to define the blood vessel edge. Areas having low values, which clearly don't belong to a blood vessel are identified, and the largest circle that lies outside the identified areas is defined. In an alternative embodiment, a largest circle that can be defined that fits into the valid (bright) area is defined. An arbitration process may be used to determine which approach should be used for each blood vessel. A blood vessel center consisting of a number of pixels can be defined, in particular when a cross section is elongated. In an exemplary embodiment, the blood vessel center is reduced to a single pixel.

In an operation 250, areas of calcium are identified in each blood vessel. Any high precision calcium identification method can be used. In an exemplary embodiment, a cross section based analysis aimed at location of the calcium seeds is performed, and a volume based analysis aimed at removal of spurious seeds created by the “salt noise” and by the growing of valid seeds into the correct calcium area is performed. With reference to FIG. 8, exemplary operations associated with identifying the areas of calcium, if any, in each blood vessel are described in accordance with an exemplary embodiment. Additional, fewer, or different operations may be performed, depending on the embodiment. The order of presentation of the operations is not intended to be limiting. In an operation 800, a first slice is selected from the heart region data. In an operation 802, a calcium threshold is applied to the first slice. In an exemplary embodiment, the calcium threshold is 150 intensity levels above the blood vessel lumen level. Adaptive threshold values taking into account expected lumen values are used. In an operation 804, a morphological filter is applied to the thresholded first slice based on the non-concentric nature of the calcium deposits. Empirical observations indicate that calcium tends to appear close to the blood vessel borders.

In an operation 806, a maximum intensity in a given cross-section is identified as a possible location of a calcium seed. In an operation 808, a distance from the center to the maximum intensity is calculated. In an operation 810, a determination is made concerning whether or not the calculated distance exceeds a calcium distance threshold. The calcium distance threshold is based on a comparison with an estimated radius value. If the distance does not exceed the calcium distance threshold, processing continues in an operation 814. If the distance does exceed the calcium distance threshold, processing continues in an operation 812. In operation 812, an area of the calcium seed is calculated. In operation 814, a determination is made concerning whether or not any vessels remain for processing. If vessels remain, processing continues at operation 808. If no vessels remain, processing continues at an operation 816. In operation 816, a determination concerning whether or not any slices remain for processing is performed. If no slices remain, processing continues at an operation 820. If slices remain, processing continues at an operation 818. In operation 818, the next slice is selected from the heart region data and processing continues at operation 802. In operation 820, a volume of any identified calcium seed(s) is calculated based on the area calculated for each slice and the number of slices over which the identified calcium seed(s) extends. If a calcium seed is identified, it also is extended to the surrounding high intensity areas providing that no “spill to the center” occurs. An extent of the calcium seed may be determined based on a threshold. For example, lumen intensities exceeding approximately 650 HU may be considered to be calcified plaque or part of the calcium seed.

With reference again to FIG. 2, in an operation 252, areas of soft plaque are identified in each blood vessel by the low intensity inside the blood vessel area. Any high precision soft plaque identification method can be used. With reference to FIG. 9, exemplary operations associated with identifying the areas of soft plaque, if any, in each blood vessel are described in accordance with an exemplary embodiment. Additional, fewer, or different operations may be performed, depending on the embodiment. The order of presentation of the operations is not intended to be limiting. In an operation 900, a first slice is selected from the heart region data. In an operation 902, a soft plaque threshold is applied to the first slice. In an exemplary embodiment, the soft plaque threshold is between approximately 50 HU and approximately 200 HU. Adaptive threshold values taking into account expected lumen values are used in an exemplary embodiment. In an operation 904, a determination is made concerning whether or not calcium is present. The presence of calcium may indicate the presence of the frequent figure “8” shaped pattern. In the figure “8” shaped pattern, calcium is located in one of the ovals of the “8”. A lumen is located in the other oval of the “8”, and soft plaque connects the two ovals. If calcium is present, processing continues at an operation 906. If calcium is not present, processing continues at an operation 908. In operation 906, a soft plaque area is identified and processing continues at an operation 911.

In operation 908, a determination is made concerning whether or not a half-moon structure is located in the blood vessel lumen. If a half-moon structure is identified from the determination, processing continues at an operation 910. If a half-moon structure is not identified from the determination, processing continue at operation 911. In operation 910, a soft plaque area is identified. In operation 911, an area of the identified soft plaque is calculated. In operation 912, a determination concerning whether or not any slices remain for processing is performed. If no slices remain, processing continues at an operation 916. If slices remain, processing continues at an operation 914. In operation 914, the next slice is selected from the heart region data, and processing continues at operation 902. In operation 916, a volume of any identified soft plaque area(s) is calculated based on the area calculated for each slice and the number of slices over which the identified soft plaque area(s) extends. In an operation 918, a volume of any identified calcium seed(s) is updated to include areas between the calcium seed and the blood vessel border and between the calcium and soft plaque areas to compensate for natural intensity low passing that may have occurred during the CT image acquisition.

With reference again to FIG. 2, in an operation 254, a severity of any obstructions identified containing soft plaque or calcium is calculated. Any obstruction computation method can be used. In an exemplary embodiment, a total obstruction ratio is calculated as a ratio of the total calcium and soft plaque areas divided by the total blood vessel area excluding the border area. In an exemplary embodiment, an obstruction is identified to be severe if the total obstruction ratio exceeds 50% for at least two consecutive cross sections. An examining physician may be allowed to control the threshold to achieve a system sensitivity matching their clinical requirements.

In some pathological cases, the cross section images may appear reasonably normal. In these cases, pathology must be identified based on the analysis of global variations. In an operation 256, global filters are applied to identify pathologies. For example, a first filter may be applied to identify a rapid decrease in the blood vessel radius. A second filter may be applied to identify a rapid decrease in the lumen intensity. A third filter may be applied to identify a rapid increase in the lumen intensity. The decisions from the series of filters may be cumulative. As a result, it is sufficient if a pathology is identified through use of one of the three filters. The filters may use the values of blood vessel radius and luminance as computed above. Use of the global filters takes into account that even healthy vessels feature significant radius and luminance variations in particular due to natural narrowing of the blood vessels, rapid changes in the vicinity of bifurcations (especially after the bifurcations), noise (in particular for relatively narrow vessels), etc. Anomalies identified by the global filters are discarded, if located in the vicinity of any bifurcations.

In an operation 258, pathology test data are calculated to determine a validity of the pathology identification process. For example, the number of identified pathologies per blood vessel segment is calculated. In an operation 260, the calculated data is compared to a quality control parameter associated with valid pathology test data. For example, a statistically expected number of detected pathologies per blood vessel segment for a specific groups of patients is determined and compared with the calculated pathology test data. In an operation 262, a determination is made concerning whether or not the pathology identification process is valid. For example, a significant deviation from the statistically expected number of detected pathologies per blood vessel segment for a specific groups of patients indicates invalidity. If the pathology identification process is valid, processing continues at an operation 264. If the pathology identification process is not valid, processing continues at operation 324 shown with reference to FIG. 3 wherein the message indicates an invalidity of the pathology identification process.

The operations described with reference to FIGS. 2-9 have been applied to a set of 50 clinical patient studies. The same studies were analyzed by expert radiologists. Overall, 200 blood vessels were analyzed. Out of this benchmark, 42 cases were identified as having severe pathologies. The remaining 158 cases were deemed to have no pathologies or only moderate pathologies. Pathology identification application 112 identified all of the severe cases correctly with a false alarm rate of 11%.

With reference again to FIG. 2, in an operation 264, a summary report is provided to a user based on the processes described with reference to FIGS. 2-9. With reference to FIG. 11, a first user interface 1100 of visualization application 114 is shown. First user interface 1100 may include a header portion 1102. Header portion 1102 may include patient data, study data, and/or acquisition data. For example, data displayed in header portion 1102 may be obtained from a header of the DICOM data. First user interface 1100 further may include a blood vessel list portion 1104. Blood vessel list portion 1104 may include a list of the blood vessels in the created blood vessel tree. Displayed next to a name identifying each blood vessel may be information related to each blood vessel including a lumen status, a total number of lesions, a number of calcium lesions, and a number of soft plaque lesions. The lumen status may indicate “normal” or a percentage of blockage that may be a percentage range. If a plurality of lesions are present, the range may indicate the maximum blockage range. A maximum volume and Agatston score may be displayed for the calcium lesions. A maximum volume and score also may be displayed for the soft plaque lesions.

User selection of a blood vessel 1106 in blood vessel list portion 1104 may cause display of a detailed description of the lesions associated with the selected blood vessel in a detail portion 1108. Detail portion 1108 may include a list of the lesions. For each lesion, a segment name, a lesion type, a degree of stenosis value, a volume, a distance from the aorta, a distance from the blood vessel origin, an eccentricity, a degree of positive remodeling, and a morphological regularity may be shown. First user interface 1100 further may include a totals portion 1110. Totals portion 1110 may include summary data associated with a degree of stenosis, lesions, the number of stents, etc.

With reference again to FIG. 2, in an operation 266, a visualization of the blood vessels is provided to a user based on the processes described with reference to FIGS. 2-9. With reference to FIG. 12, a second user interface 1200 of visualization application 114 in accordance with a first exemplary embodiment is shown. In the exemplary embodiment of FIG. 12, four simultaneous views of the same pathology may be shown to facilitate a correct diagnosis with each view presented in a different area of second user interface 1200. Each view may be created using a variety of graphical user interface techniques in a common window, in separate windows, or in any combination of windows. Second user interface 1200 may include a first axial slice viewer 1202, a first 3-D coronary vessel map 1204, a first stretched blood vessel image 1206, and an intra-vascular ultrasound (IVUS) type view 1208. First axial slice viewer 1202 presents intensity levels from a slice of imaging data. The intensity levels may be indicated in color or gray-scale. For example, first axial slice viewer 1202 may indicate an identified pathology 1203 in red. First axial slice viewer 1202 may present the slice of imaging data in a top left area of second user interface 1200.

First 3-D coronary vessel map 1204 provides a view of the blood vessel tree synchronized with first axial slice viewer 1202 to indicate the identified pathology 1203. First 3-D coronary vessel map 1204 may be presented in a top right area of second user interface 1200 and may include a 3-D grid to identify the length of the blood vessels in the blood vessel tree in each direction. Selecting an area of first stretched blood vessel image 1206 may cause image rotation of first 3-D coronary vessel map 1204 around its axis to facilitate a correct 3-D perception of the blood vessel structure. First 3-D coronary vessel map 1204 may be synchronized with first axial slice viewer 1202 to distinguish the selected blood vessel from the remaining blood vessels in the blood vessel tree. First 3-D coronary vessel map 1204 may be rotated using an input interface as known to those skilled in the art. Indicators may be provided in first 3-D coronary vessel map 1204 to indicate end points and bifurcations. For example, end points may be indicated using green circles and bifurcations may be indicated using red circles.

First stretched blood vessel image 1206 includes a vertical bar which denotes a location of the slice displayed in first axial slice viewer 1202 in a stretched view of a selected blood vessel. First stretched blood vessel image 1206 may be located in a bottom left area of second user interface 1200. The physician can superimpose corresponding plaque areas. For example, soft plaque may be indicated in red and calcified plaque indicated in blue. If desired, the physician can invoke an edit mode and correct automatic results.

With reference to FIG. 13, a third user interface of visualization application 114 graphically displays a lumen area of each blood vessel to clearly identify all stenosis lesions and to allow an evaluation of their severity. The graphical display includes a distance from the aorta on the X-axis and a lumen area on the Y-axis. A first curve 1300 indicates a normal blood vessel lumen. A second curve 1302 indicates a stenosis due to calcified plaque. A third curve 1304 indicates a stenosis due to soft plaque.

With reference to FIG. 14, a fourth user interface 1400 of visualization application 114 in accordance with a second exemplary embodiment is shown. Fourth user interface 1400 may include a second axial slice viewer 1402, a second 3-D coronary vessel map 1404, a second stretched blood vessel image 1406, and a pathology report type view 1408. Second axial slice viewer 1402 may include an axial slice of the imaging data presented in a top left area of second user interface 1400. provides a current location on the 3-D coronary vessel map synchronized with second axial slice viewer 1402. Second 3-D coronary vessel map 1404 may be presented in a top right area of second user interface 1400 and may include a 3-D grid to identify the length of the blood vessels in the blood vessel tree in each direction. Selecting an area of second 3-D coronary vessel map 1404 may cause image rotation around its axis facilitating a correct 3-D perception of the blood vessel structure.

Second stretched blood vessel image 1406 includes a vertical bar which denotes a location of the slice displayed in second axial slice viewer 1402 in a stretched view of a selected blood vessel. Second stretched blood vessel image 1406 may be presented in a bottom left area of second user interface 1400.

Pathology report type view 1408 may contain a pathology list 1409 of detected pathologies based on the processes described with reference to FIGS. 2-9. The pathologies may include soft plaque, calcified plaque, and mixed plaque regions. The location and stenosis level may be included for each pathology in pathology list 1409. Selecting a pathology 1410 from pathology list 1409 of pathology report type view 1408 may cause a synchronized display of pathology 1410 in second axial slice viewer 1402, second 3-D coronary vessel map 1404, and second stretched blood vessel image 1406. For example, second axial slice viewer 1402 includes a first pathology indicator 1412, which indicates the location of pathology 1410 in second axial slice viewer 1402. Second 3-D coronary vessel map 1404 includes a second pathology indicator 1414, which indicates the location of pathology 1410 in the 3-D coronary artery tree view. Second stretched blood vessel image 1406 includes a first point 1416 and a second point 1418, which indicate the location of pathology 1410 in second stretched blood vessel image 1406.

With reference to FIG. 15, a fifth user interface 1500 of visualization application 114 in accordance with a third exemplary embodiment is shown. Fifth user interface 1500 may include a third axial slice viewer 1502 and a third stretched blood vessel image 1504. Third axial slice viewer 1502 is synchronized with third stretched blood vessel image 1504. Third axial slice 1502 presents intensity levels from an axial slice of imaging data. The intensity levels may be indicated in color or gray-scale. For example, third axial slice viewer 1502 may indicate an identified pathology 1506 in red. Third stretched blood vessel image 1504 presents intensity levels of a blood vessel selected from third axial slice viewer 1502 and shown in stretched form. Third stretched blood vessel image 1504 may includes a vertical bar 1508 which denotes a location of the slice presented in third axial slice viewer 1502. When the user selects a vessel area in third axial slice viewer 1502, third stretched blood vessel image 1504 shows a stretched presentation of the appropriate vessel. When the user selects an area in third stretched blood vessel image 1504, third axial slice viewer 1502 displays the appropriate slice of the patient study.

In an exemplary embodiment, fifth user interface 1500 may initially include third axial slice viewer 1502. When the user selects an artery from third axial slice viewer 1502, the selected blood vessel is presented in third stretched blood vessel image 1504 with vertical bar 1508 denoting the location of the slice presented in third axial slice viewer 1502. Execution of one or more of the processes described with reference to FIGS. 2-9 may be performed after selection of the blood vessel to identify the stretched blood vessel presented in third stretched blood vessel image 1504. As a result, using a single “click” the user may trigger a determination of all relevant segments of the blood vessel from the slices and reconstruct the stretched blood vessel for presentation in third stretched blood vessel image 1504.

Fifth user interface 1500 further may include an axial presentation only button 1510, a new study selection button 1512, a save current screen button 1514, and an exit program button 1516. User selection of axial presentation only button 1510 causes stretched blood vessel image 1504 to be removed from fifth user interface 1500. User selection of new study selection button 1512 causes presentation of a selection window that allows the user to select a new patient study for analysis. User selection of save current screen button 1514 causes presentation of a save window that allows the user to select a location and a name for a file to which the contents of fifth user interface 1500 are saved for review, for printing, for sending with a message, etc. User selection of exit program button 1516 may cause fifth user interface 1500 to close.

With reference to FIG. 16, exemplary operations associated with assessing a quality of the medical imaging data are described. Additional, fewer, or different operations may be performed, depending on the embodiment. The order of presentation of the operations is not intended to be limiting. The operations described with reference to FIG. 16 may be performed before execution of pathology identification application 112 and/or visualization application 114 (described with reference to FIG. 1) or integrated as part of execution of pathology identification application 112 and/or visualization application 114. For example, the exemplary operations may be performed after operation 210 described with reference to FIGS. 2 a and 2 b to determine a success or failure of the image enhancement process. In an operation 1600, an image resolution is calculated. Image resolution may be stored in the image DICOM header and defined based on the “distance between slices” and “pixel size” fields. In an operation 1602, the calculated image resolution is compared with a quality control parameter such as an image resolution threshold. An exemplary image resolution threshold may be based on an inter-slice distance below 0.7 mm and a pixel size below 0.5 mm. In an operation 1604, a determination is made concerning whether or not the image resolution is high enough. If the calculated image resolution is high enough, processing continues at an operation 1606. If the calculated image resolution is not high enough, processing continues at operation 1624.

In operation 1606, a determination is made concerning whether or not the heart region is fully visible. A zoom in used in reconstruction should cause the heart region to occupy most of the region of interest (ROI). The heart region may be automatically estimated and, if it is significantly smaller than the ROI, it may be determined that the heart region is not fully visible. If the heart region is fully visible, processing continues at an operation 1608. If the heart region is not fully visible, processing continues at operation 1624. In operation 1608, a determination is made concerning whether or not the contrast injection density is acceptable. The volume of contrast may be estimated by thresholding the image data for example at 100 HU. If the determined volume of contrast is lower than a predefined threshold such as 30 mm×30 mm×20 mm, it may be determined that the contrast injection density is unacceptable. If the contrast injection density is acceptable, processing continues at an operation 1610. If the contrast injection density is not acceptable, processing continues at operation 1624.

The image may be searched for various artifacts using specialized filters. In operation 1610, a determination is made concerning whether or not motion artifacts exist. If motion artifacts do not exist, processing continues at an operation 1612. If motion artifacts exist, processing continues at operation 1624. In operation 1612, a determination is made concerning whether or not reconstruction artifacts exist. If reconstruction artifacts do not exist, processing continues at an operation 1614. If reconstruction artifacts exist, processing continues at operation 1624. In operation 1614, a determination is made concerning whether or not physical artifacts exist. If physical artifacts do not exist, processing continues at an operation 1616. If physical artifacts exist, processing continues at operation 1624.

In operation 1616, an image signal-to-noise ratio (SNR) is calculated. In an operation 1618, the calculated SNR is compared with a quality control parameter such as a SNR threshold. The image SNR may be computed as [(M_(IV)−M_(IB))/σ_(IB),]² where M_(IV) is the mean of the intensity in the vessel, M_(IB) is the mean of the intensity of the background, and σ_(IB) is the standard deviation of the intensity of the background. The vessel and background locations may be coarsely estimated in order to collect the statistics. In an operation 1620, a determination is made concerning whether or not the calculated SNR is high enough. In an exemplary embodiment, a SNR threshold of 100 dB is used. If the calculated SNR is high enough, processing continues at an operation 1622. In operation 1622, processing continues. In operation 1624, a message is output to the user. For example, a message may be printed from computing device 102, may be displayed at computing device 102, may played audibly from computing device 102, may be transmitted to the user (i.e., e-mail, text message, instant message, voice message, etc.), etc. The message may indicate an assessment of the image data quality based on the test condition which failed. In an operation 1626, processing of the CT image data stops.

One or more quality control parameters may be selected by the user for example using a user interface presented using pathology identification application 112 and/or visualization application 114. The selected quality control parameters may be defined and/or stored in a configuration file stored at computing device 102 or on another computing device accessible from computing device 102. The quality control parameters may be defined as a function of the type of medical diagnostic equipment used to generate the imaging data. The quality control parameters may be predefined.

Comparison with quality control parameters allows supervision of the quality of the results produced by pathology identification application 112 and/or visualization application 114. A failure of a test comparison performed on a partial result may indicate either a total failure of the automatic processing or a low confidence in the result. In the former case, the processing should be stopped. In the latter case, the confidence level should be propagated to the next processing level and finally presented to the user. Each module of the processing pipeline in addition to the expected output generates a number from zero to one designating the module confidence in the result. For each module, the confidence is computed differently and the computation depends on the particular algorithm. The overall confidence of the system in the final results is computed as the product of confidence levels produced by each module. A failure may be identified when a confidence level exceeds a threshold. The failure in compliance with a required quality level of the input imaging data should prevent the system from automatic processing of the study and warn the operator about the low quality of the acquired data. The operator may decide in such case to perform a repeat examination. In addition, the image quality control system can be used as a separate module (i.e. without pathology identification application 112 and/or visualization application 114) in a medical imaging facility as a tool for automatic acquisition quality control and CT machine calibration.

The word “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Further, for the purposes of this disclosure and unless otherwise specified, “a” or “an” means “one or more”.

The foregoing description of exemplary embodiments of the invention have been presented for purposes of illustration and of description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed, and modifications and variations are possible in light of the above teachings or may be acquired from practice of the invention. The functionality described may be implemented in a single executable or application or may be distributed among modules that differ in number and distribution of functionality from those described herein. Additionally, the order of execution of the functions may be changed depending on the embodiment. The embodiments were chosen and described in order to explain the principles of the invention and as practical applications of the invention to enable one skilled in the art to utilize the invention in various embodiments and with various modifications as suited to the particular use contemplated. It is intended that the scope of the invention be defined by the claims appended hereto and their equivalents. 

1. A system for identifying a data quality issue associated with medical diagnostic data, the system comprising: an imaging apparatus configured to generate imaging data including a heart region and an aorta region of a patient; and a processor operably coupled to the imaging apparatus to receive the generated imaging data, the processor configured to: analyze the received imaging data; identify a data quality issue associated with the received imaging data based on the analysis of the received imaging data; generate a message including the identified data quality issue; and send the generated message to a user, wherein the data quality issue is identified by comparing the analyzed imaging data with a quality control parameter and identifying the data quality issue based on the comparison, and wherein the quality control parameter is a number of pathologies detected per blood vessel segment defined from the analyzed imaging data.
 2. A non-transitory computer-readable medium with programmed instructions that, upon execution by a processor, cause the processor to identify a data quality issue associated with medical diagnostic data, the instructions comprising: receiving imaging data; analyzing the received imaging data; identifying a data quality issue associated with the received imaging data based on the analysis of the received imaging data; generating a message including the identified data quality issue; and sending the generated message to a user, wherein identifying the data quality issue comprises: comparing the analyzed imaging data with a quality control parameter; and identifying the data quality issue based on the comparison, wherein the quality control parameter is a number of pathologies detected per blood vessel segment defined from the analyzed imaging data.
 3. A method of identifying a data quality issue associated with medical diagnostic data, the method comprising: receiving imaging data at a computing device; analyzing the received imaging data at the computing device; identifying a data quality issue associated with the received imaging data based on the analysis of the received imaging data at the computing device; generating a message including the identified data quality issue at the computing device; and sending the generated message to a user, wherein identifying the data quality issue comprises: comparing the analyzed imaging data with a quality control parameter; and identifying the data quality issue based on the comparison, wherein the quality control parameter is a number of pathologies detected per blood vessel segment defined from the analyzed imaging data.
 4. The method of claim 3, wherein sending the generated message to the user is a method selected from the group consisting of printing the generated message, displaying the generated message, and playing audibly the generated message.
 5. The method of claim 3, wherein sending the generated message comprises a text message.
 6. The method of claim 3, wherein the imaging data is computed tomography data.
 7. The method of claim 3, wherein the imaging data is computed tomography angiography data.
 8. The method of claim 3, wherein the generated message includes an assessment of the image data quality.
 9. The method of claim 3, wherein the assessment is a characteristic selected from the group consisting of a signal-to-noise ratio value, an image resolution value, a physical artifact indicator, a reconstruction artifact indicator, a motion artifact indicator, and a contrast injection density value.
 10. The method of claim 3, wherein the assessment of the image data quality is based on a determination that a heart region is not fully visible.
 11. The method of claim 3, wherein the quality control parameter is a size of a heart region defined in the analyzed imaging data.
 12. The method of claim 3, wherein the quality control parameter is a location of a heart region in the analyzed imaging data.
 13. The method of claim 3, wherein the quality control parameter is an intensity level of a lung region defined in the analyzed imaging data.
 14. The method of claim 3, wherein the quality control parameter is a volume of contrast defined in the analyzed imaging data.
 15. The method of claim 3, wherein the quality control parameter is a position of an object in a heart region defined from the analyzed imaging data.
 16. The method of claim 3, wherein the quality control parameter is an orientation of an object in a heart region defined from the analyzed imaging data.
 17. The method of claim 3, wherein the quality control parameter is a position of an object relative to an aorta object defined in the analyzed imaging data.
 18. The method of claim 3, wherein the quality control parameter is a size of an object defined in the analyzed imaging data. 